R Bootcamp, Module 6: Data manipulation with the tidyverse
August 2018, UC Berkeley
Nima Hejazi (based on materials developed by Kellie Ottoboni, Rochelle Terman, and Chris Krogslund)
Overview
It is often said that 80% of data analysis is spent on the process of cleaning and preparing the data. (Dasu and Johnson, 2003)
Thus before you can even get to doing any sort of sophisticated analysis or plotting, you’ll generally first need to:
- Manipulating data frames, e.g., filtering, summarizing, and conducting calculations across groups.
- Tidying data into the appropriate format
There are two competing schools of thought within the R community.
- We should stick to the base R functions to do manipulating and tidying;
tidyverse uses syntax that’s unlike base R and is superfluous.
- We should start teaching students to manipulate data using
tidyverse tools because they are straightfoward to use, more readable than base R, and speed up the tidying process.
We’ll show you some of the tidyverse tools so you can make an informed decision about whether you want to use base R or these newfangled packages.
Example Dataset
For this unit, we’ll be working with the “Gapminder” dataset, which is excerpt of the data available at Gapminder.org. For each of 142 countries, the data provides values for life expectancy, GDP per capita, and population, every five years, from 1952 to 2007.
## country year pop continent lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134
Data frame Manipulation using Base R Functions
So far, you’ve seen the basics of manipulating data frames, e.g. subsetting, merging, and basic calculations. For instance, we can use base R functions to calculate summary statistics across groups of observations, e.g., the mean GDP per capita within each region:
## [1] 2193.755
## [1] 7136.11
## [1] 7902.15
But this isn’t ideal because it involves a fair bit of repetition. Repeating yourself will cost you time, both now and later, and potentially introduce some nasty bugs.
Data frame Manipulation using dplyr
Luckily, the dplyr package provides a number of very useful functions for manipulating data frames. These functions will save you time by reducing repetition. As an added bonus, you might even find the dplyr grammar easier to read.
Here we’re going to cover 6 of the most commonly used functions as well as using pipes (%>%) to combine them.
select()filter()group_by()summarize()mutate()arrange()
If you have have not installed this package earlier, please do so now:
Now let’s load the package:
dplyr::select
Imagine that we just received the gapminder dataset, but are only interested in a few variables in it. We could use the select() function to keep only the columns corresponding to variables we select.
## year country gdpPercap
## 1 1952 Afghanistan 779.4453
## 2 1957 Afghanistan 820.8530
## 3 1962 Afghanistan 853.1007
## 4 1967 Afghanistan 836.1971
## 5 1972 Afghanistan 739.9811
## 6 1977 Afghanistan 786.1134
If we open up year_country_gdp, we’ll see that it only contains the year, country and gdpPercap. This is equivalent to the base R subsetting function:
## year country gdpPercap
## 1 1952 Afghanistan 779.4453
## 2 1957 Afghanistan 820.8530
## 3 1962 Afghanistan 853.1007
## 4 1967 Afghanistan 836.1971
## 5 1972 Afghanistan 739.9811
## 6 1977 Afghanistan 786.1134
We can even check that these two data frames are equivalent:
## [1] TRUE
But, as we will see, dplyr makes for much more readable, efficient code because of its pipe operator.
piping with dplyr
Above, we used what’s called “normal” grammar, but the strengths of dplyr lie in combining several functions using pipes. Pipes take the input on the left side of the %>% symbol and pass it in as the first argument to the function on the right side. Since the pipe grammar is unlike anything we’ve seen in R before, let’s repeat what we’ve done above using pipes.
First we summon the gapminder dataframe and pass it on to the next step using the pipe symbol %>% The second steps is the select() function. In this case we don’t specify which data object we use in the call to select() since we’ve piped it in.
Fun Fact: There is a good chance you have encountered pipes before in the shell. In R, a pipe symbol is %>% while in the shell it is |. But the concept is the same!
dplyr::filter
Now let’s say we’re only interested in African countries. We can combine select and filter to select only the observations where continent is Africa.
As with last time, first we pass the gapminder data frame to the filter() function, then we pass the filtered version of the gapminder data frame to the select() function.
To clarify, both the select and filter functions subsets the data frame. The difference is that select extracts certain columns, while filter extracts certain rows.
Note: The order of operations is very important in this case. If we used ‘select’ first, filter would not be able to find the variable continent since we would have removed it in the previous step.
dplyr Calculations Across Groups
A common task you’ll encounter when working with data is running calculations on different groups within the data. For instance, what if we wanted to calculate the mean GDP per capita for each continent?
In base R, you would have to run the mean() function for each subset of data:
## [1] 2193.755
## [1] 7136.11
## [1] 7902.15
## [1] 14469.48
## [1] 18621.61
That’s a lot of repetition! To make matters worse, what if we wanted to add these values to our original data frame as a new column? We would have to write something like this:
You can see how this can get pretty tedious, especially if we want to calculate more complicated or refined statistics. We could use loops or apply functions, but these can be difficult, slow, or error-prone.
dplyr split-apply-combine
The abstract problem we’re encountering here is know as “split-apply-combine”:
We want to split our data into groups (in this case continents), apply some calculations on each group, then combine the results together afterwards.
Module 4 gave some ways to do split-apply-combine type stuff using the apply family of functions, but those are error prone and messy.
Luckily, dplyr offers a much cleaner, straight-forward solution to this problem.
dplyr::group_by
We’ve already seen how filter() can help us select observations that meet certain criteria (in the above: continent == "Europe"). More helpful, however, is the group_by() function, which will essentially use every unique criteria that we could have used in filter().
A grouped_df can be thought of as a list where each item in the list is a data.frame which contains only the rows that correspond to the a particular value continent (at least in the example above).
dplyr::summarize
group_by() on its own is not particularly interesting. It’s much more exciting used in conjunction with the summarize() function. This will allow use to create new variable(s) by applying transformations to variables in each of the continent-specific data frames. In other words, using the group_by() function, we split our original data frame into multiple pieces, which we then apply summary functions to (e.g. mean() or sd()) within summarize(). The output is a new data frame reduced in size, with one row per group.
## # A tibble: 5 x 2
## continent mean_gdpPercap
## <fct> <dbl>
## 1 Africa 2194.
## 2 Americas 7136.
## 3 Asia 7902.
## 4 Europe 14469.
## 5 Oceania 18622.
That allowed us to calculate the mean gdpPercap for each continent. But it gets even better – the function group_by() allows us to group by multiple variables. Let’s group by year and continent.
## # A tibble: 6 x 3
## # Groups: continent [1]
## continent year mean_gdpPercap
## <fct> <int> <dbl>
## 1 Africa 1952 1253.
## 2 Africa 1957 1385.
## 3 Africa 1962 1598.
## 4 Africa 1967 2050.
## 5 Africa 1972 2340.
## 6 Africa 1977 2586.
That is already quite powerful, but it gets even better! You’re not limited to defining 1 new variable in summarize().
## # A tibble: 6 x 6
## # Groups: continent [1]
## continent year mean_gdpPercap sd_gdpPercap mean_pop sd_pop
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Africa 1952 1253. 983. 4570010. 6317450.
## 2 Africa 1957 1385. 1135. 5093033. 7076042.
## 3 Africa 1962 1598. 1462. 5702247. 7957545.
## 4 Africa 1967 2050. 2848. 6447875. 8985505.
## 5 Africa 1972 2340. 3287. 7305376. 10130833.
## 6 Africa 1977 2586. 4142. 8328097. 11585184.
dplyr::mutate
What if we wanted to add these values to our original data frame instead of creating a new object? For this, we can use the mutate() function, which is similar to summarize() except it creates new variables to the same data frame that you pass into it.
## # A tibble: 6 x 10
## # Groups: continent, year [6]
## country year pop continent lifeExp gdpPercap mean_gdpPercap
## <fct> <int> <dbl> <fct> <dbl> <dbl> <dbl>
## 1 Afghan… 1952 8.43e6 Asia 28.8 779. 5195.
## 2 Afghan… 1957 9.24e6 Asia 30.3 821. 5788.
## 3 Afghan… 1962 1.03e7 Asia 32.0 853. 5729.
## 4 Afghan… 1967 1.15e7 Asia 34.0 836. 5971.
## 5 Afghan… 1972 1.31e7 Asia 36.1 740. 8187.
## 6 Afghan… 1977 1.49e7 Asia 38.4 786. 7791.
## # ... with 3 more variables: sd_gdpPercap <dbl>, mean_pop <dbl>,
## # sd_pop <dbl>
We can use also use mutate() to create new variables prior to (or even after) summarizing information.
## # A tibble: 6 x 8
## # Groups: continent [1]
## continent year mean_gdpPercap sd_gdpPercap mean_pop sd_pop
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Africa 1952 1253. 983. 4570010. 6.32e6
## 2 Africa 1957 1385. 1135. 5093033. 7.08e6
## 3 Africa 1962 1598. 1462. 5702247. 7.96e6
## 4 Africa 1967 2050. 2848. 6447875. 8.99e6
## 5 Africa 1972 2340. 3287. 7305376. 1.01e7
## 6 Africa 1977 2586. 4142. 8328097. 1.16e7
## # ... with 2 more variables: mean_gdp_billion <dbl>, sd_gdp_billion <dbl>
dplyr::arrange
As a last step, let’s say we want to sort the rows in our data frame according to values in a certain column. We can use the arrange() function to do this. For instance, let’s organize our rows by year (recent first), and then by continent.
## # A tibble: 6 x 10
## # Groups: continent, year [1]
## country year pop continent lifeExp gdpPercap mean_gdpPercap
## <fct> <int> <dbl> <fct> <dbl> <dbl> <dbl>
## 1 Algeria 2007 3.33e7 Africa 72.3 6223. 3089.
## 2 Angola 2007 1.24e7 Africa 42.7 4797. 3089.
## 3 Benin 2007 8.08e6 Africa 56.7 1441. 3089.
## 4 Botswa… 2007 1.64e6 Africa 50.7 12570. 3089.
## 5 Burkin… 2007 1.43e7 Africa 52.3 1217. 3089.
## 6 Burundi 2007 8.39e6 Africa 49.6 430. 3089.
## # ... with 3 more variables: sd_gdpPercap <dbl>, mean_pop <dbl>,
## # sd_pop <dbl>
dplyr Take-aways
- Human readable: the function names describe the action being done
- Piping: chain functions in a step-by-step way, rather than nesting
- Facilitates split-apply-combine manipulations
Tidying Data
Even before we conduct analysis or calculations, we need to put our data into the correct format. The goal here is to rearrange a messy dataset into one that is tidy
The two most important properties of tidy data are:
- Each column is a variable.
- Each row is an observation.
Tidy data is easier to work with, because you have a consistent way of referring to variables (as column names) and observations (as row indices). It then becomes easy to manipulate, visualize, and model.
For more on the concept of tidy data, read Hadley Wickham’s paper here
Tidying Data/Wide vs. Long Formats
“Tidy datasets are all alike but every messy dataset is messy in its own way.” – Hadley Wickham
Tabular datasets can be arranged in many ways. For instance, consider the data below. Each data set displays information on heart rate observed in individuals across 3 different time periods. But the data are organized differently in each table.
wide <- data.frame(
name = c("Wilbur", "Petunia", "Gregory"),
time1 = c(67, 80, 64),
time2 = c(56, 90, 50),
time3 = c(70, 67, 101)
)
wide
## name time1 time2 time3
## 1 Wilbur 67 56 70
## 2 Petunia 80 90 67
## 3 Gregory 64 50 101
long <- data.frame(
name = c("Wilbur", "Petunia", "Gregory", "Wilbur", "Petunia", "Gregory", "Wilbur", "Petunia", "Gregory"),
time = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
heartrate = c(67, 80, 64, 56, 90, 50, 70, 67, 10)
)
long
## name time heartrate
## 1 Wilbur 1 67
## 2 Petunia 1 80
## 3 Gregory 1 64
## 4 Wilbur 2 56
## 5 Petunia 2 90
## 6 Gregory 2 50
## 7 Wilbur 3 70
## 8 Petunia 3 67
## 9 Gregory 3 10
Question: Which one of these do you think is the tidy format?
Answer: The first dataframe (the “wide” one) would not be considered tidy because values (i.e., heartrate) are spread across multiple columns.
We often refer to these different structurs as “long” vs. “wide” formats. In the “long” format, you usually have 1 column for the observed variable and the other columns are ID variables.
For the “wide” format each row is often a site/subject/patient and you have multiple observation variables containing the same type of data. These can be either repeated observations over time, or observation of multiple variables (or a mix of both). In the above case, we had the same kind of data (heart rate) entered across 3 different columns, corresponding to three different time periods.
You may find data input may be simpler and some programs/functions may prefer the “wide” format. However, many of R’s functions have been designed assuming you have “long” format data.
Tidying the Gapminder Data
Lets look at the structure of our original gapminder dataframe:
## country year pop continent lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134
Question: Is this data frame wide or long?
Answer: This data frame is somewhere in between the purely ‘long’ and ‘wide’ formats. We have 3 “ID variables” (continent, country, year) and 3 “Observation variables” (pop, lifeExp, gdpPercap).
Despite not having ALL observations in 1 column, this intermediate format makes sense given that all 3 observation variables have different units. As we have seen, many of the functions in R are often vector based, and you usually do not want to do mathematical operations on values with different units.
On the other hand, there are some instances in which a purely long or wide format is ideal (e.g. plotting). Likewise, sometimes you’ll get data on your desk that is poorly organized, and you’ll need to reshape it.
tidyr
Thankfully, the tidyr package will help you efficiently transform your data regardless of original format.
tidyr::gather
Until now, we’ve been using the nicely formatted original gapminder data set. This data set is not quite wide and not quite long – it’s something in the middle, but “real” data (i.e., our own research data) will never be so well organized. Here let’s start with the wide format version of the gapminder data set.
## continent country gdpPercap_1952 gdpPercap_1957 gdpPercap_1962
## 1 Africa Algeria 2449.0082 3013.9760 2550.8169
## 2 Africa Angola 3520.6103 3827.9405 4269.2767
## gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982
## 1 3246.9918 4182.6638 4910.4168 5745.1602
## 2 5522.7764 5473.2880 3008.6474 2756.9537
## gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002
## 1 5681.3585 5023.2166 4797.2951 5288.0404
## 2 2430.2083 2627.8457 2277.1409 2773.2873
## gdpPercap_2007 lifeExp_1952 lifeExp_1957 lifeExp_1962 lifeExp_1967
## 1 6223.3675 43.077 45.685 48.303 51.407
## 2 4797.2313 30.015 31.999 34.000 35.985
## lifeExp_1972 lifeExp_1977 lifeExp_1982 lifeExp_1987 lifeExp_1992
## 1 54.518 58.014 61.368 65.799 67.744
## 2 37.928 39.483 39.942 39.906 40.647
## lifeExp_1997 lifeExp_2002 lifeExp_2007 pop_1952 pop_1957 pop_1962
## 1 69.152 70.994 72.301 9279525 10270856 11000948
## 2 40.963 41.003 42.731 4232095 4561361 4826015
## pop_1967 pop_1972 pop_1977 pop_1982 pop_1987 pop_1992 pop_1997 pop_2002
## 1 12760499 14760787 17152804 20033753 23254956 26298373 29072015 31287142
## 2 5247469 5894858 6162675 7016384 7874230 8735988 9875024 10866106
## pop_2007
## 1 33333216
## 2 12420476
## [ reached getOption("max.print") -- omitted 4 rows ]
The first step towards getting our nice intermediate data format is to first convert from the wide to the long format. The function gather() will ‘gather’ the observation variables into a single variable. This is sometimes called “melting” your data, because it melts the table from wide to long. Those data will be melted into two variables: one for the variable names, and the other for the variable values.
## continent country obstype_year obs_values
## 1 Africa Algeria gdpPercap_1952 2449.0082
## 2 Africa Angola gdpPercap_1952 3520.6103
## 3 Africa Benin gdpPercap_1952 1062.7522
## 4 Africa Botswana gdpPercap_1952 851.2411
## 5 Africa Burkina Faso gdpPercap_1952 543.2552
## 6 Africa Burundi gdpPercap_1952 339.2965
Notice that we put 3 arguments into the gather() function:
- the name the new column for the new ID variable (
obstype_year),
- the name for the new amalgamated observation variable (
obs_value),
- the indices of the old variables (
3:38, signalling columns 3 through 38) that we want to gather into one variable. Notice that we don’t want to melt down columns 1 and 2, as these are considered “ID” variables.
tidyr::select
If there are a lot of columns or they’re named in a consistent pattern, we might not want to select them using the column numbers. It’d be easier to use some information contained in the names themselves. We can select variables using:
- variable indices
- variable names (without quotes)
x:z to select all variables between x and z-y to exclude ystarts_with(x, ignore.case = TRUE): all names that starts with xends_with(x, ignore.case = TRUE): all names that ends with xcontains(x, ignore.case = TRUE): all names that contain x
See the select() function in dplyr for more options.
For instance, here we do the same gather operation with (1) the starts_with function, and (2) the - operator:
## continent country obstype_year obs_values
## 1 Africa Algeria pop_1952 9279525
## 2 Africa Angola pop_1952 4232095
## 3 Africa Benin pop_1952 1738315
## 4 Africa Botswana pop_1952 442308
## 5 Africa Burkina Faso pop_1952 4469979
## 6 Africa Burundi pop_1952 2445618
## continent country obstype_year obs_values
## 1 Africa Algeria gdpPercap_1952 2449.0082
## 2 Africa Angola gdpPercap_1952 3520.6103
## 3 Africa Benin gdpPercap_1952 1062.7522
## 4 Africa Botswana gdpPercap_1952 851.2411
## 5 Africa Burkina Faso gdpPercap_1952 543.2552
## 6 Africa Burundi gdpPercap_1952 339.2965
However you choose to do it, notice that the output collapses all of the measure variables into two columns: one containing new ID variable, the other containing the observation value for that row.
tidyr::separate
You’ll notice that in our long dataset, obstype_year actually contains 2 pieces of information, the observation type (pop, lifeExp, or gdpPercap) and the year.
We can use the separate() function to split the character strings into multiple variables:
## continent country obs_type year obs_values
## 1 Africa Algeria gdpPercap 1952 2449.0082
## 2 Africa Angola gdpPercap 1952 3520.6103
## 3 Africa Benin gdpPercap 1952 1062.7522
## 4 Africa Botswana gdpPercap 1952 851.2411
## 5 Africa Burkina Faso gdpPercap 1952 543.2552
## 6 Africa Burundi gdpPercap 1952 339.2965
If you didn’t use tidyr to do this, you’d have to use the strsplit function and use multiple lines of code to replace the column in gap_long with two new columns. This solution is much cleaner.
tidyr::spread
The opposite of gather() is spread(). It spreads our observation variables back out to make a wider table. We can use this function to spread our gap_long() to the original “medium” format.
## continent country year gdpPercap lifeExp pop
## 1 Africa Algeria 1952 2449.008 43.077 9279525
## 2 Africa Algeria 1957 3013.976 45.685 10270856
## 3 Africa Algeria 1962 2550.817 48.303 11000948
## 4 Africa Algeria 1967 3246.992 51.407 12760499
## 5 Africa Algeria 1972 4182.664 54.518 14760787
## 6 Africa Algeria 1977 4910.417 58.014 17152804
All we need is some quick fixes to make this dataset identical to the original gapminder dataset:
## continent country year gdpPercap lifeExp pop
## 1 Africa Algeria 1952 2449.008 43.077 9279525
## 2 Africa Algeria 1957 3013.976 45.685 10270856
## 3 Africa Algeria 1962 2550.817 48.303 11000948
## 4 Africa Algeria 1967 3246.992 51.407 12760499
## 5 Africa Algeria 1972 4182.664 54.518 14760787
## 6 Africa Algeria 1977 4910.417 58.014 17152804
## country year pop continent lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134
## country year pop continent lifeExp gdpPercap
## 1 Algeria 1952 9279525 Africa 43.077 2449.008
## 2 Algeria 1957 10270856 Africa 45.685 3013.976
## 3 Algeria 1962 11000948 Africa 48.303 2550.817
## 4 Algeria 1967 12760499 Africa 51.407 3246.992
## 5 Algeria 1972 14760787 Africa 54.518 4182.664
## 6 Algeria 1977 17152804 Africa 58.014 4910.417
## country year pop continent lifeExp gdpPercap
## 1 Afghanistan 1952 8425333 Asia 28.801 779.4453
## 2 Afghanistan 1957 9240934 Asia 30.332 820.8530
## 3 Afghanistan 1962 10267083 Asia 31.997 853.1007
## 4 Afghanistan 1967 11537966 Asia 34.020 836.1971
## 5 Afghanistan 1972 13079460 Asia 36.088 739.9811
## 6 Afghanistan 1977 14880372 Asia 38.438 786.1134
Breakout
dplyr
Use dplyr to create a data frame containing the median lifeExp for each continent
Use dplyr to add a column to the gapminder dataset that contains the total population of the continent of each observation in a given year. For example, if the first observation is Afghanistan in 1952, the new column would contain the population of Asia in 1952.
Use dplyr to: add a column called gdpPercap_diff that contains the difference between the observation’s gdpPercap and the mean gdpPercap of the continent in that year. Arrange the dataframe by the column you just created, in descending order (so that the relatively richest country/years are listed first)
hint: You might have to ungroup() before you arrange().
tidyr
- Subset the results from question #3 to select only the
country, year, and gdpPercap_diff columns. Use tidyr put it in wide format so that countries are rows and years are columns.